There are several other studies concerning representation learning for network samples related to our current work. Perhaps most closely associated with the PCAN and sPCAN algorithms are embedding algorithms designed for multilayered networks including multi-node2vec [1]} and the multilayered embedding methods described in [2]}. These strategies are descriptive methods that generate embeddings for networks with multiple layers, where network layers generally represent differing edge types among a similar set of vertices. Network samples are a special case of multilayered networks where layers are treated as independent observations. There is also some recent work focused on modeling samples of networks, including the random effects stochastic block model [3]} the multi-subject stochastic block model [4]}, the edge-based logistic model [5]}, and the hierarchical latent space model [6]}. Each of these strategies are generative embedding procedures for network samples that depend on the estimation of a probabilistic model describing the sample.
